Analyzing urban influence on extreme winter precipitation through observations and numerical simulation of two South China case studies.

This study investigates the impact of urbanization on extreme winter rainfall in the South China Greater Bay Area (GBA) through the analysis of hourly station observations and simulations using the Weather Research and Forecasting Model with the Single Layer Urban Canopy Model (WRF-SLUCM). Data from 2008 to 2017 reveal that urban areas in the GBA experience lower 99th percentile hourly winter rainfall intensity compared to surrounding rural regions. However, urban locations exhibit higher annual maximum hourly rainfall (Rmax) and very extreme rainfall events (99.99th percentile) in winter, suggesting a positive influence of urbanization on extreme winter precipitation. A case study further underscores the role of the Urban Heat Island (UHI) effect in enhancing extreme rainfall intensity and probability in the GBA urban areas. Additionally, two extreme cases were dynamically downscaled using WRF-SLUCM, involving four parallel experiments: replacing urban land use with cropland (Nourban), using historical urban land use data from 1999 (99LS), projecting near-future urban land use for 2030 (30LS), and considering 2030 urban land use without anthropogenic heat (AH) (30LS-AH0). Synoptic analysis demonstrates that cold air intrusion suppresses the GBA UHI in Case 2013 but not in Case 2015. Reduced evaporation and humidity induced by urban surfaces significantly decrease urban precipitation in Case 2013. In contrast, the persistent UHI in Case 2015 enhances local convection and land-ocean circulation, leading to increased moisture flux convergence and amplified urban precipitation intensity and probability in 30LS compared to Nourban. This amplification is primarily attributed to AH, while the change in 99LS remains insignificant. These findings suggest that urban influences on extreme precipitation in the GBA persist during winter, particularly when the UHI effect is maintained.

DownScaleBench for developing and applying a deep learning based urban climate downscaling- first results for high-resolution urban precipitation climatology over Austin, Texas.

Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 - 10 km) and neighborhood (order of 0.1 - 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This 'DownScaleBench' tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.

Improving the local climate zone classification with building height, imperviousness, and machine learning for urban models.

The Local Climate Zone (LCZ) classification is already widely used in urban heat island and other climate studies. The current classification method does not incorporate crucial urban auxiliary GIS data on building height and imperviousness that could significantly improve urban-type LCZ classification utility as well as accuracy. This study utilized a hybrid GIS- and remote sensing imagery-based framework to systematically compare and evaluate different machine and deep learning methods. The Convolution Neural Network (CNN) classifier outperforms in terms of accuracy, but it requires multi-pixel input, which reduces the output's spatial resolution and creates a tradeoff between accuracy and spatial resolution. The Random Forest (RF) classifier performs best among the single-pixel classifiers. This study also shows that incorporating building height dataset improves the accuracy of the high- and mid-rise classes in the RF classifiers, whereas an imperviousness dataset improves the low-rise classes. The single-pass forward permutation test reveals that both auxiliary datasets dominate the classification accuracy in the RF classifier, while near-infrared and thermal infrared are the dominating features in the CNN classifier. These findings show that the conventional LCZ classification framework used in the World Urban Database and Access Portal Tools (WUDAPT) can be improved by adopting building height and imperviousness information. This framework can be easily applied to different cities to generate LCZ maps for urban models.

Accelerating flash droughts induced by the joint influence of soil moisture depletion and atmospheric aridity.

The emergence of flash drought has attracted widespread attention due to its rapid onset. However, little is known about the recent evolution of flash droughts in terms of the speed of onset and the causes of such a rapid onset phase of flash droughts. Here, we present a comprehensive assessment of the onset development of flash droughts and the underlying mechanisms on a global scale. We find that 33.64-46.18% of flash droughts with 5-day onset of drying, and there is a significant increasing trend in the proportion of flash droughts with the 1-pentad onset time globally during the period 2000-2020. Flash droughts do not appear to be occurring more frequently in most global regions, just coming on faster. In addition, atmospheric aridity is likely to create a flash drought-prone environment, and the joint influence of soil moisture depletion and atmospheric aridity further accelerates the rapid onset of flash droughts.

[Effects of astragaloside â £ on inflammatory response and percentage of peripheral blood Th17 cells in mice with ulcerative colitis].

This study aimed to investigate the anti-inflammatory effect of astragaloside Ⅳ in mice with ulcerative colitis(UC) and its effect on the percentage of peripheral blood T helper(Th17) cells. Following the establishment of UC mouse model with 2% sodium dextran sulfate(DSS), mice in the positive control group and low-and high-dose astragaloside Ⅳ groups were treated with corresponding drugs by gavage. Disease activity index(DAI) was calculated, and serum interleukin-17(IL-17), tumor necrosis factor-α(TNF-α), and transforming growth factor-ß(TGF-ß) levels were assayed by ELISA. The pathological changes in colon tissue were observed by HE staining, and Th17/regulatory T cells(Treg) ratio in the peripheral blood was determined by flow cytometry. Western blot was conducted for detecting the relative protein expression levels of forkhead box protein P3(Foxp3) and retinoic acid-related orphan nuclear receptor γT(ROR-γt). The findings demonstrated that in normal mice, the colonic structure was intact. The goblet cells were not reduced and the glands were neatly arranged, with no mucosal erosion, bleeding, or positive cell infiltration. In the model group, the colonic mucosal structure was seriously damaged, manifested as disordered arrangement or missing of glands, vascular dilatation, congestion, and massive inflammatory cell infiltration. The pathological injury of colon tissue was alleviated to varying degrees in drug treatment groups. Compared with the normal group, the model group exhibited elevated percentage of Th17 cells, increased IL-17 and TNF-α content, up-regulated relative ROR-γt protein expression, lowered TGF-ß, reduced percentage of Treg cells, and down-regulated relative Foxp3 protein expression. The comparison with the model group showed that DAI score, pathological score, percentage of Th17 cells, IL-17 and TNF-α content, and relative ROR-γt protein expression in the positive control group, low-dose astragaloside Ⅳ group, and high-dose astragaloside Ⅳ group were decreased, while TGF-ß content, percentage of Treg cells, and relative Foxp3 protein expression were increased. The DAI score, pathological score, percentage of Th17 cells, IL-17 and TNF-α content, and relative ROR-γt protein expression in the low-dose astragaloside Ⅳ group were higher than those in the positive control group, whereas the content of TGF-ß, percentage of Treg cells, and relative Foxp3 protein expression were lower. DAI score, pathological score, percentage of Th17 cells, IL-17 and TNF-α content, relative ROR-γt protein expression in the high-dose astragaloside Ⅳ group declined in contrast to those in the low-dose astragaloside Ⅳ group, while the TGF-ß content, percentage of Treg cells, and relative Foxp3 protein expression rose. There was no significant difference between the positive control group and the high-dose astragaloside Ⅳ group. Astragaloside Ⅳ is able to inhibit inflammatory response and diminish the percentage of Th17 cells in mice with UC.

Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979-2100).

Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979-2014 and future scenarios (SSP245 and SSP585) for 2015-2100 with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that the bias-corrected data are of better quality than the individual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth's future climate, atmospheric environment, hydrology, agriculture, wind power, etc.

Divergent effects of climate change on future groundwater availability in key mid-latitude aquifers.

Groundwater provides critical freshwater supply, particularly in dry regions where surface water availability is limited. Climate change impacts on GWS (groundwater storage) could affect the sustainability of freshwater resources. Here, we used a fully-coupled climate model to investigate GWS changes over seven critical aquifers identified as significantly distressed by satellite observations. We assessed the potential climate-driven impacts on GWS changes throughout the 21st century under the business-as-usual scenario (RCP8.5). Results show that the climate-driven impacts on GWS changes do not necessarily reflect the long-term trend in precipitation; instead, the trend may result from enhancement of evapotranspiration, and reduction in snowmelt, which collectively lead to divergent responses of GWS changes across different aquifers. Finally, we compare the climate-driven and anthropogenic pumping impacts. The reduction in GWS is mainly due to the combined impacts of over-pumping and climate effects; however, the contribution of pumping could easily far exceed the natural replenishment.

Emergent spectral properties of river network topology: an optimal channel network approach.

Characterization of river drainage networks has been a subject of research for many years. However, most previous studies have been limited to quantities which are loosely connected to the topological properties of these networks. In this work, through a graph-theoretic formulation of drainage river networks, we investigate the eigenvalue spectra of their adjacency matrix. First, we introduce a graph theory model for river networks and explore the properties of the network through its adjacency matrix. Next, we show that the eigenvalue spectra of such complex networks follow distinct patterns and exhibit striking features including a spectral gap in which no eigenvalue exists as well as a finite number of zero eigenvalues. We show that such spectral features are closely related to the branching topology of the associated river networks. In this regard, we find an empirical relation for the spectral gap and nullity in terms of the energy dissipation exponent of the drainage networks. In addition, the eigenvalue distribution is found to follow a finite-width probability density function with certain skewness which is related to the drainage pattern. Our results are based on optimal channel network simulations and validated through examples obtained from physical experiments on landscape evolution. These results suggest the potential of the spectral graph techniques in characterizing and modeling river networks.

[Correlation between Signs of Living Body in Abdominal and Pelvic Cavities and Syndrome Typing of Chinese Medicine in Colorectal Cancer Patients].

OBJECTIVE: To explore the correlation between signs of living body in abdominal and pelvic cavities and syndrome typing of Chinese medicine (CM) in colorectal cancer patients. METHODS: Totally 112 colorectal cancer patients undergoing open abdominal surgery or laporoscopic surgery were syndrome typed as five types, i.e., inner-accumulation of damp and heat, blockage of stasis and toxin, Pi-Shen yang deficiency, blood-qi deficiency, Gan-Shen yin deficiency. Signs of living body in abdominal and pelvic cavities were collected. The correlation between signs of living body in abdominal and pelvic cavities and syndrome typing of CM were analyzed. RESULTS: Red colorectal canals or mass were dominated in colorectal cancer patients with inner-accumulation of damp and heat syndrome. Dark purple colorectal canals or mass were dominated in colorectal cancer patients with blockage of stasis and toxin syndrome. Reddish colorectal canals or mass were dominated in colorectal cancer patients with blood-qi deficiency syndrome. Pale colorectal canals or mass were dominated in colorectal cancer patients with Pi-Shen yang deficiency syndrome. Whitish or red-white stripes were dominated in colorectal cancer patients with Gan-Shen yin deficiency syndrome. Dropsy colorectal canal was associated with Pi-Shen yang deficiency syndrome. Intracavitary effusion was often seen in colorectal cancer patients with inner-accumulation of damp and heat syndrome. The effusion was yellowish in less amount. Intracavitary adhesion was often seen in colorectal cancer patients with blockage of stasis and toxin syndrome. There was no correlation between the maximum diameter of mass or each syndrome type of CM. CONCLUSION: There existed correlation between signs of living body in abdominal and pelvic cavities and syndrome typing of CM, which could be taken as one of references for syndrome typing of colorectal cancer patients.

High sensitivity of Indian summer monsoon to Middle East dust absorptive properties.

The absorptive properties of dust aerosols largely determine the magnitude of their radiative impacts on the climate system. Currently, climate models use globally constant values of dust imaginary refractive index (IRI), a parameter describing the dust absorption efficiency of solar radiation, although it is highly variable. Here we show with model experiments that the dust-induced Indian summer monsoon (ISM) rainfall differences (with dust minus without dust) change from -9% to 23% of long-term climatology as the dust IRI is changed from zero to the highest values used in the current literature. A comparison of the model results with surface observations, satellite retrievals, and reanalysis data sets indicates that the dust IRI values used in most current climate models are too low, tending to significantly underestimate dust radiative impacts on the ISM system. This study highlights the necessity for developing a parameterization of dust IRI for climate studies.